class_docstr: The prediction error visualizer plots the actual targets from the dataset
  against the predicted values generated by our model(s). This visualizer is
  used to detect noise or heteroscedasticity along a range of the target
  domain.
  
  Parameters
  ----------
  
  estimator : a Scikit-Learn regressor
      Should be an instance of a regressor, otherwise will raise a
      YellowbrickTypeError exception on instantiation.
      If the estimator is not fitted, it is fit when the visualizer is fitted,
      unless otherwise specified by ``is_fitted``.
  
  ax : matplotlib Axes, default: None
      The axes to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  shared_limits : bool, default: True
      If shared_limits is True, the range of the X and Y axis limits will
      be identical, creating a square graphic with a true 45 degree line.
      In this form, it is easier to diagnose under- or over- prediction,
      though the figure will become more sparse. To localize points, set
      shared_limits to False, but note that this will distort the figure
      and should be accounted for during analysis.
  
  bestfit : bool, default: True
      Draw a linear best fit line to estimate the correlation between the
      predicted and measured value of the target variable. The color of
      the bestfit line is determined by the ``line_color`` argument.
  
  identity : bool, default: True
      Draw the 45 degree identity line, y=x in order to better show the
      relationship or pattern of the residuals. E.g. to estimate if the
      model is over- or under- estimating the given values. The color of the
      identity line is a muted version of the ``line_color`` argument.
  
  alpha : float, default: 0.75
      Specify a transparency where 1 is completely opaque and 0 is completely
      transparent. This property makes densely clustered points more visible.
  
  is_fitted : bool or str, default='auto'
      Specify if the wrapped estimator is already fitted. If False, the estimator
      will be fit when the visualizer is fit, otherwise, the estimator will not be
      modified. If 'auto' (default), a helper method will check if the estimator
      is fitted before fitting it again.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  
  score_ : float
      The R^2 score that specifies the goodness of fit of the underlying
      regression model to the test data.
  
  Examples
  --------
  
  >>> from yellowbrick.regressor import PredictionError
  >>> from sklearn.linear_model import Lasso
  >>> model = PredictionError(Lasso())
  >>> model.fit(X_train, y_train)
  >>> model.score(X_test, y_test)
  >>> model.show()
  
  Notes
  -----
  
  PredictionError is a ScoreVisualizer, meaning that it wraps a model and
  its primary entry point is the `score()` method.
code: def __init__(
      self,
      estimator,
      ax=None,
      shared_limits=True,
      bestfit=True,
      identity=True,
      alpha=0.75,
      is_fitted="auto",
      **kwargs,
  ):
      # Initialize the visualizer
      super(PredictionError, self).__init__(
          estimator, is_fitted=is_fitted, ax=ax, **kwargs
      )
  
      # Visual arguments
      self.colors = {
          "point": kwargs.pop("point_color", None),
          "line": kwargs.pop("line_color", LINE_COLOR),
      }
  
      # Drawing arguments
      self.shared_limits = shared_limits
      self.bestfit = bestfit
      self.identity = identity
      self.alpha = alpha
  
file_path: yellowbrick/yellowbrick/regressor/prediction_error.py
imports: from yellowbrick.style.palettes import LINE_COLOR
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.bestfit import draw_best_fit, draw_identity_line
  from yellowbrick.regressor.base import RegressionScoreVisualizer
label_desc: Use image node_id 1 to create a new PredictionError object from inherited base classes: RegressionScoreVisualizer with example: obj = PredictionError(estimator, ax, shared_limits, bestfit, identity, alpha, is_fitted)